Workshop: Exploring the InsideAirBnB dataset

Preamble

# Clear workspace
rm(list=ls()); graphics.off() 

### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.
library(magrittr) # For extra-piping operators (eg. %<>%)
library(skimr) # For nice data summaries

The InsideAirBnB data

Instroduction

• The data is sourced from the Inside Airbnb which hosts publicly available data from the Airbnb site.
• Interactive visualizations are provided here

The dataset comprises of three main tables:

• listings - Detailed listings data showing 96 atttributes for each of the listings. Some of the attributes which are intuitivly interesting are: price (continuous), longitude (continuous), latitude (continuous), listing_type (categorical), is_superhost (categorical), neighbourhood (categorical), ratings (continuous) among others.
• reviews - Detailed reviews given by the guests with 6 attributes. Key attributes include date (datetime), listing_id (discrete), reviewer_id (discrete) and comment (textual).
• calendar - Provides details about booking for the next year by listing. Four attributes in total including listing_id (discrete), date (datetime), available (categorical) and price (continuous).

Load data

listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/listings.csv.gz')
listings %>% glimpse()

Rows: 28,523
Columns: 106
$ id                                           <dbl> 6983, 26057, 26473, 29118, 29618, 31094, 32379, 32841, 33850, 3…
$ listing_url                                  <chr> "https://www.airbnb.com/rooms/6983", "https://www.airbnb.com/ro…
$ scrape_id                                    <dbl> 20200626200423, 20200626200423, 20200626200423, 20200626200423,…
$ last_scraped                                 <date> 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-29, 20…
$ name                                         <chr> "Copenhagen 'N Livin'", "Lovely house - most attractive area", …
$ summary                                      <chr> "Lovely apartment located in the hip Nørrebro area, close to ba…
$ space                                        <chr> "Beautiful and cosy apartment conveniently located in the hip N…
$ description                                  <chr> "Lovely apartment located in the hip Nørrebro area, close to ba…
$ experiences_offered                          <chr> "none", "none", "none", "none", "none", "none", "none", "none",…
$ neighborhood_overview                        <chr> "Nice bars and cozy cafes just minutes away, yet the street its…
$ notes                                        <chr> NA, NA, NA, NA, "Please note that the bed in the second bedroom…
$ transit                                      <chr> "Bus 66 runs to the central station. Forum metro is about 10 mi…
$ access                                       <chr> "Bedroom, living room, kitchen, and bathroom for shared use. Yo…
$ interaction                                  <chr> "We are usually at work during day time, but will be home most …
$ house_rules                                  <chr> "No smoking allowed! No pets.", "We will leave the house clean …
$ thumbnail_url                                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ medium_url                                   <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ picture_url                                  <chr> "https://a0.muscache.com/im/pictures/42044170/f63c4d99_original…
$ xl_picture_url                               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ host_id                                      <dbl> 16774, 109777, 112210, 125230, 127577, 129976, 140105, 142143, …
$ host_url                                     <chr> "https://www.airbnb.com/users/show/16774", "https://www.airbnb.…
$ host_name                                    <chr> "Simon", "Kari", "Oliver", "Nana", "Simon And Anna", "Ebbe", "L…
$ host_since                                   <date> 2009-05-12, 2010-04-17, 2010-04-22, 2010-05-15, 2010-05-18, 20…
$ host_location                                <chr> "Copenhagen, Capital Region of Denmark, Denmark", "Copenhagen, …
$ host_about                                   <chr> "I'm currently working as an environmental consultant for a lar…
$ host_response_time                           <chr> "N/A", "N/A", "within a few hours", "N/A", "N/A", "N/A", "withi…
$ host_response_rate                           <chr> "N/A", "N/A", "100%", "N/A", "N/A", "N/A", "100%", "N/A", "N/A"…
$ host_acceptance_rate                         <chr> "33%", "19%", "100%", "17%", "N/A", "N/A", "97%", "0%", "N/A", …
$ host_is_superhost                            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ host_thumbnail_url                           <chr> "https://a0.muscache.com/im/users/16774/profile_pic/1401276934/…
$ host_picture_url                             <chr> "https://a0.muscache.com/im/users/16774/profile_pic/1401276934/…
$ host_neighbourhood                           <chr> "Nørrebro", "Indre By", "Indre By", "Vesterbro", "Østerbro", "V…
$ host_listings_count                          <dbl> 1, 1, 4, 1, 1, 1, 3, 1, 0, 2, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, …
$ host_total_listings_count                    <dbl> 1, 1, 4, 1, 1, 1, 3, 1, 0, 2, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, …
$ host_verifications                           <chr> "['email', 'phone', 'reviews']", "['email', 'phone', 'reviews',…
$ host_has_profile_pic                         <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
$ host_identity_verified                       <lgl> FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRU…
$ street                                       <chr> "Copenhagen, Hovedstaden, Denmark", "Copenhagen, Hovedstaden, D…
$ neighbourhood                                <chr> "Nørrebro", "Indre By", "Indre By", "Vesterbro", "Østerbro", "V…
$ neighbourhood_cleansed                       <chr> "Nrrebro", "Indre By", "Indre By", "Vesterbro-Kongens Enghave",…
$ neighbourhood_group_cleansed                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ city                                         <chr> "Copenhagen", "Copenhagen", "Copenhagen", "Copenhagen", "Copenh…
$ state                                        <chr> "Hovedstaden", "Hovedstaden", "Hovedstaden", "Hovedstaden", "Ho…
$ zipcode                                      <chr> "2200", "2100", "1210", "1650", "2100", "1719", "1620", "2100",…
$ market                                       <chr> "Copenhagen", "Copenhagen", "Copenhagen", "Copenhagen", "Copenh…
$ smart_location                               <chr> "Copenhagen, Denmark", "Copenhagen, Denmark", "Copenhagen, Denm…
$ country_code                                 <chr> "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK…
$ country                                      <chr> "Denmark", "Denmark", "Denmark", "Denmark", "Denmark", "Denmark…
$ latitude                                     <dbl> 55.68798, 55.69163, 55.67590, 55.67069, 55.69375, 55.66744, 55.…
$ longitude                                    <dbl> 12.54571, 12.57459, 12.57698, 12.55430, 12.56945, 12.55516, 12.…
$ is_location_exact                            <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TR…
$ property_type                                <chr> "Apartment", "House", "House", "Apartment", "Apartment", "Apart…
$ room_type                                    <chr> "Private room", "Entire home/apt", "Entire home/apt", "Entire h…
$ accommodates                                 <dbl> 2, 6, 12, 2, 4, 3, 3, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 6,…
$ bathrooms                                    <dbl> 1.0, 1.5, 2.5, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0…
$ bedrooms                                     <dbl> 1, 4, 6, 1, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, …
$ beds                                         <dbl> 1, 4, 7, 1, 3, 3, 2, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 4, …
$ bed_type                                     <chr> "Real Bed", "Real Bed", "Real Bed", "Real Bed", "Real Bed", "Re…
$ amenities                                    <chr> "{TV,\"Cable TV\",Wifi,Kitchen,\"Paid parking off premises\",He…
$ square_feet                                  <dbl> 97, NA, NA, NA, NA, 689, NA, 807, NA, 420, 161, NA, 527, NA, NA…
$ price                                        <chr> "$365.00", "$2,398.00", "$3,096.00", "$797.00", "$857.00", "$75…
$ weekly_price                                 <chr> NA, NA, "$17,513.00", NA, "$2,981.00", "$4,700.00", "$7,453.00"…
$ monthly_price                                <chr> NA, NA, "$67,073.00", NA, "$8,943.00", NA, "$26,084.00", "$14,1…
$ security_deposit                             <chr> "$0.00", "$5,000.00", "$3,726.00", NA, NA, "$1,000.00", "$0.00"…
$ cleaning_fee                                 <chr> "$33.00", "$1,100.00", "$522.00", "$300.00", "$75.00", NA, "$0.…
$ guests_included                              <dbl> 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 3, 2, 4, …
$ extra_people                                 <chr> "$66.00", "$350.00", "$0.00", "$0.00", "$0.00", "$100.00", "$33…
$ minimum_nights                               <dbl> 2, 3, 3, 7, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ maximum_nights                               <dbl> 15, 30, 31, 14, 31, 10, 365, 1125, 21, 90, 30, 730, 14, 15, 112…
$ minimum_minimum_nights                       <dbl> 2, 3, 3, 3, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ maximum_minimum_nights                       <dbl> 2, 3, 3, 5, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ minimum_maximum_nights                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ maximum_maximum_nights                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ minimum_nights_avg_ntm                       <dbl> 2.0, 3.0, 3.0, 4.1, 7.0, 2.0, 3.0, 6.0, 5.0, 30.0, 1.0, 3.0, 5.…
$ maximum_nights_avg_ntm                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ calendar_updated                             <chr> "5 months ago", "4 months ago", "7 months ago", "4 months ago",…
$ has_availability                             <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
$ availability_30                              <dbl> 29, 28, 29, 21, 0, 0, 8, 0, 11, 0, 0, 0, 2, 29, 29, 0, 0, 6, 0,…
$ availability_60                              <dbl> 59, 58, 59, 21, 0, 0, 8, 0, 24, 0, 0, 0, 13, 59, 59, 0, 0, 36, …
$ availability_90                              <dbl> 89, 88, 89, 21, 0, 0, 8, 5, 24, 26, 0, 0, 43, 89, 89, 0, 0, 66,…
$ availability_365                             <dbl> 89, 363, 172, 21, 0, 58, 8, 189, 24, 301, 0, 0, 133, 364, 89, 0…
$ calendar_last_scraped                        <date> 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-29, 20…
$ number_of_reviews                            <dbl> 168, 50, 293, 22, 90, 17, 73, 7, 40, 16, 184, 9, 74, 67, 92, 7,…
$ number_of_reviews_ltm                        <dbl> 1, 4, 31, 2, 0, 0, 1, 0, 0, 1, 11, 1, 2, 1, 13, 0, 0, 11, 0, 0,…
$ first_review                                 <date> 2009-09-04, 2013-12-02, 2010-10-14, 2010-06-17, 2010-08-16, 20…
$ last_review                                  <date> 2019-07-19, 2019-12-14, 2020-03-02, 2019-08-02, 2017-06-03, 20…
$ review_scores_rating                         <dbl> 96, 98, 91, 98, 94, 97, 98, 91, 97, 89, 92, 98, 94, 95, 94, 100…
$ review_scores_accuracy                       <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10, 9, 9, 9, 10, 9, …
$ review_scores_cleanliness                    <dbl> 9, 10, 9, 10, 9, 10, 10, 9, 9, 8, 8, 9, 9, 9, 9, 10, 9, 10, 10,…
$ review_scores_checkin                        <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
$ review_scores_communication                  <dbl> 10, 10, 10, 10, 9, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 10, 1…
$ review_scores_location                       <dbl> 9, 10, 10, 10, 10, 10, 10, 9, 9, 10, 10, 9, 10, 10, 9, 10, 9, 1…
$ review_scores_value                          <dbl> 9, 10, 9, 10, 9, 9, 9, 9, 10, 9, 9, 9, 9, 9, 9, 10, 9, 10, 10, …
$ requires_license                             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ license                                      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ jurisdiction_names                           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ instant_bookable                             <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FA…
$ is_business_travel_ready                     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ cancellation_policy                          <chr> "moderate", "moderate", "moderate", "strict_14_with_grace_perio…
$ require_guest_profile_picture                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ require_guest_phone_verification             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ calculated_host_listings_count               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ calculated_host_listings_count_entire_homes  <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, …
$ calculated_host_listings_count_private_rooms <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
$ calculated_host_listings_count_shared_rooms  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ reviews_per_month                            <dbl> 1.28, 0.62, 2.48, 0.18, 0.75, 0.14, 0.61, 0.06, 0.33, 0.14, 1.5…

#calendar <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/calendar.csv.gz')
#calendar %>% glimpse()

#reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/reviews.csv.gz')
#reviews %>% glimpse()

# # And the summary plus geodata
# summaries_listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/listings.csv')
# summaries_reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/reviews.csv')
# summaries_neighbourhoods <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.csv')

# The geodat of the hoods comes as a geojson, so we need the right package to load it
library(geojsonio)
neighbourhoods_geojson <- geojson_read( 'http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.geojson',  what = "sp")

Problem 1: Professional host

listings %>%
  count(host_id, sort = TRUE)

listings %>%
  filter(host_id == 187610263) %>%
  count(neighbourhood_cleansed, sort = TRUE)

listings %<>%
  mutate(price = price %>% parse_number(),
         price_sqf = price / square_feet) 

listings %<>%
  group_by(host_id) %>%
  mutate(host_professional = n() >= 5) %>%
  ungroup()

listings %>%
  group_by(host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE),
            price = price %>% mean(na.rm = TRUE))

listings %>%
  group_by(neighbourhood_cleansed, host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE)) %>%
  pivot_wider(names_from = host_professional, values_from = review)

Description & Satisfaction

listings %<>%
  mutate(desc_lenght = description %>% str_count('\\w+')) %>%
  mutate(desc_long =  percent_rank(desc_lenght) > 0.9 )

listings %>%
  group_by(desc_long) %>%
  summarise(review = review_scores_rating %>% mean(na.rm =TRUE))

Inspecting & Tidying data

Basic formating

listings %>% skim()

── Data Summary ────────────────────────
                           Values    
Name                       Piped data
Number of rows             28523     
Number of columns          110       
_______________________              
Column type frequency:               
  character                45        
  Date                     5         
  logical                  18        
  numeric                  42        
________________________             
Group variables            None      

── Variable type: character ───────────────────────────────────────────────────────────────────────────────────────────
   skim_variable          n_missing complete_rate   min   max empty n_unique whitespace
 1 listing_url                    0        1         33    37     0    28523          0
 2 name                          57        0.998      1   211     0    26906          0
 3 summary                     1096        0.962      1  1000     0    26981          0
 4 space                      11390        0.601      1  1000     0    16804          0
 5 description                  515        0.982      1  1000     0    27748          0
 6 experiences_offered            0        1          4     4     0        1          0
 7 neighborhood_overview      12407        0.565      1  1000     0    15514          0
 8 notes                      20832        0.270      1  1000     0     7365          0
 9 transit                    11316        0.603      1  1000     0    16711          0
10 access                     15751        0.448      1  1000     0    11249          0
11 interaction                14111        0.505      1  1000     0    13726          0
12 house_rules                13332        0.533      1  1000     0    13703          0
13 picture_url                    0        1         81   146     0    28282          0
14 host_url                       0        1         37    43     0    25745          0
15 host_name                     12        1.00       1    34     0     6415          0
16 host_location                 93        0.997      2   151     0      863          0
17 host_about                 14028        0.508      1  3550     0    12476         34
18 host_response_time            11        1.00       3    18     0        5          0
19 host_response_rate            11        1.00       2     4     0       44          0
20 host_acceptance_rate          11        1.00       2     4     0      100          0
21 host_thumbnail_url            11        1.00      55   106     0    25669          0
22 host_picture_url              11        1.00      57   109     0    25669          0
23 host_neighbourhood          8007        0.719      1    21     0       56          0
24 host_verifications             0        1          2   156     0      316          0
25 street                         0        1         10    61     0      527          0
26 neighbourhood                  0        1          5    14     0       21          0
27 neighbourhood_cleansed         0        1          5    25     0       11          0
28 city                          13        1.00       1    26     0      134          0
29 state                      24042        0.157      1    25     0      170          0
30 zipcode                      810        0.972      3    17     0      454          0
31 market                       864        0.970      6    21     0       10          0
32 smart_location                 0        1         10    35     0      157          0
33 country_code                   0        1          2     2     0        1          0
34 country                        0        1          7     7     0        1          0
35 property_type                  0        1          3    22     0       29          0
36 room_type                      0        1         10    15     0        4          0
37 bed_type                       0        1          5    13     0        5          0
38 amenities                      0        1          2  1179     0    26634          0
39 weekly_price               25008        0.123      7    11     0      718          0
40 monthly_price              26971        0.0544     7    11     0      476          0
41 security_deposit           13845        0.515      5    10     0      386          0
42 cleaning_fee                8968        0.686      5     9     0      430          0
43 extra_people                   0        1          5     9     0      265          0
44 calendar_updated               0        1          5    13     0       82          0
45 cancellation_policy            0        1          8    27     0        4          0

── Variable type: Date ────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable         n_missing complete_rate min        max        median     n_unique
1 last_scraped                  0         1     2020-06-26 2020-06-30 2020-06-27        5
2 host_since                   11         1.00  2008-06-27 2020-06-26 2015-06-22     3379
3 calendar_last_scraped         0         1     2020-06-26 2020-06-30 2020-06-27        5
4 first_review               4968         0.826 2009-09-04 2020-06-28 2017-08-07     2582
5 last_review                4968         0.826 2011-08-10 2020-06-28 2019-07-29     1846

── Variable type: logical ─────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable                    n_missing complete_rate      mean count                   
 1 thumbnail_url                        28523         0     NaN       ": "                    
 2 medium_url                           28523         0     NaN       ": "                    
 3 xl_picture_url                       28523         0     NaN       ": "                    
 4 host_is_superhost                       11         1.00    0.102   "FAL: 25610, TRU: 2902" 
 5 host_has_profile_pic                    11         1.00    0.998   "TRU: 28445, FAL: 67"   
 6 host_identity_verified                  11         1.00    0.376   "FAL: 17790, TRU: 10722"
 7 neighbourhood_group_cleansed         28523         0     NaN       ": "                    
 8 is_location_exact                        0         1       0.789   "TRU: 22507, FAL: 6016" 
 9 has_availability                         0         1       1       "TRU: 28523"            
10 requires_license                         0         1       0       "FAL: 28523"            
11 license                              28523         0     NaN       ": "                    
12 jurisdiction_names                   28523         0     NaN       ": "                    
13 instant_bookable                         0         1       0.286   "FAL: 20360, TRU: 8163" 
14 is_business_travel_ready                 0         1       0       "FAL: 28523"            
15 require_guest_profile_picture            0         1       0.00505 "FAL: 28379, TRU: 144"  
16 require_guest_phone_verification         0         1       0.00687 "FAL: 28327, TRU: 196"  
17 host_professional                        0         1       0.0305  "FAL: 27653, TRU: 870"  
18 desc_long                              515         0.982   0.0972  "FAL: 25285, TRU: 2723" 

── Variable type: numeric ─────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable                                n_missing complete_rate       mean            sd       p0      p25
 1 id                                                   0        1        2.04e+ 7 12261143.     6.98e+ 3 1.01e+ 7
 2 scrape_id                                            0        1        2.02e+13        0      2.02e+13 2.02e+13
 3 host_id                                              0        1        6.73e+ 7 76172316.     5.13e+ 2 1.28e+ 7
 4 host_listings_count                                 11        1.00     5.46e+ 0       34.3    0.       1.00e+ 0
 5 host_total_listings_count                           11        1.00     5.46e+ 0       34.3    0.       1.00e+ 0
 6 latitude                                             0        1        5.57e+ 1        0.0191 5.56e+ 1 5.57e+ 1
 7 longitude                                            0        1        1.26e+ 1        0.0317 1.24e+ 1 1.25e+ 1
 8 accommodates                                         0        1        3.32e+ 0        1.63   1.00e+ 0 2.00e+ 0
 9 bathrooms                                           12        1.00     1.08e+ 0        0.284  0.       1.00e+ 0
10 bedrooms                                            29        0.999    1.55e+ 0        1.06   0.       1.00e+ 0
11 beds                                               103        0.996    2.04e+ 0        1.44   0.       1.00e+ 0
12 square_feet                                      28130        0.0138   7.22e+ 2      576.     0.       1.20e+ 2
13 price                                                0        1        8.49e+ 2     1067.     0.       4.98e+ 2
14 guests_included                                      0        1        1.52e+ 0        1.06   1.00e+ 0 1.00e+ 0
15 minimum_nights                                       0        1        3.85e+ 0       18.1    1.00e+ 0 2.00e+ 0
16 maximum_nights                                       0        1        6.21e+ 2      553.     1.00e+ 0 1.50e+ 1
17 minimum_minimum_nights                               0        1        3.84e+ 0       18.1    1.00e+ 0 2.00e+ 0
18 maximum_minimum_nights                               0        1        4.12e+ 0       19.3    1.00e+ 0 2.00e+ 0
19 minimum_maximum_nights                               0        1        6.67e+ 2      548.     1.00e+ 0 2.00e+ 1
20 maximum_maximum_nights                               0        1        6.70e+ 2      547.     1.00e+ 0 2.00e+ 1
21 minimum_nights_avg_ntm                               0        1        3.97e+ 0       18.3    1.00e+ 0 2.00e+ 0
22 maximum_nights_avg_ntm                               0        1        6.68e+ 2      547.     1.00e+ 0 2.00e+ 1
23 availability_30                                      0        1        5.87e+ 0       10.4    0.       0.      
24 availability_60                                      0        1        1.12e+ 1       20.1    0.       0.      
25 availability_90                                      0        1        1.65e+ 1       30.0    0.       0.      
26 availability_365                                     0        1        4.95e+ 1       99.2    0.       0.      
27 number_of_reviews                                    0        1        1.36e+ 1       27.1    0.       1.00e+ 0
28 number_of_reviews_ltm                                0        1        2.74e+ 0        6.64   0.       0.      
29 review_scores_rating                              5447        0.809    9.52e+ 1        6.83   2.00e+ 1 9.30e+ 1
30 review_scores_accuracy                            5468        0.808    9.73e+ 0        0.655  2.00e+ 0 1.00e+ 1
31 review_scores_cleanliness                         5466        0.808    9.38e+ 0        0.956  2.00e+ 0 9.00e+ 0
32 review_scores_checkin                             5488        0.808    9.82e+ 0        0.561  2.00e+ 0 1.00e+ 1
33 review_scores_communication                       5470        0.808    9.86e+ 0        0.529  2.00e+ 0 1.00e+ 1
34 review_scores_location                            5491        0.807    9.60e+ 0        0.678  2.00e+ 0 9.00e+ 0
35 review_scores_value                               5495        0.807    9.45e+ 0        0.763  2.00e+ 0 9.00e+ 0
36 calculated_host_listings_count                       0        1        4.45e+ 0       28.1    1.00e+ 0 1.00e+ 0
37 calculated_host_listings_count_entire_homes          0        1        4.17e+ 0       28.1    0.       1.00e+ 0
38 calculated_host_listings_count_private_rooms         0        1        2.58e- 1        0.731  0.       0.      
39 calculated_host_listings_count_shared_rooms          0        1        1.22e- 2        0.325  0.       0.      
40 reviews_per_month                                 4968        0.826    4.95e- 1        0.727  1.00e- 2 1.20e- 1
41 price_sqf                                        28130        0.0138 Inf             NaN      3.36e- 1 8.22e- 1
42 desc_lenght                                        515        0.982    1.26e+ 2       56.3    0.       7.70e+ 1
        p50      p75      p100 hist 
 1 1.95e+ 7 3.04e+ 7   4.40e 7 ▇▇▇▅▆
 2 2.02e+13 2.02e+13   2.02e13 ▁▁▇▁▁
 3 3.60e+ 7 9.46e+ 7   3.52e 8 ▇▂▁▁▁
 4 1.00e+ 0 1.00e+ 0   7.37e 2 ▇▁▁▁▁
 5 1.00e+ 0 1.00e+ 0   7.37e 2 ▇▁▁▁▁
 6 5.57e+ 1 5.57e+ 1   5.57e 1 ▁▃▇▇▁
 7 1.26e+ 1 1.26e+ 1   1.26e 1 ▁▂▇▅▂
 8 3.00e+ 0 4.00e+ 0   1.60e 1 ▇▂▁▁▁
 9 1.00e+ 0 1.00e+ 0   1.00e 1 ▇▁▁▁▁
10 1.00e+ 0 2.00e+ 0   1.01e 2 ▇▁▁▁▁
11 2.00e+ 0 3.00e+ 0   2.50e 1 ▇▁▁▁▁
12 7.64e+ 2 1.08e+ 3   2.80e 3 ▇▇▃▁▁
13 6.98e+ 2 9.96e+ 2   6.92e 4 ▇▁▁▁▁
14 1.00e+ 0 2.00e+ 0   1.60e 1 ▇▁▁▁▁
15 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
16 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
17 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
18 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
19 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
20 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
21 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
22 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
23 0.       8.00e+ 0   3.00e 1 ▇▁▁▁▂
24 0.       1.40e+ 1   6.00e 1 ▇▁▁▁▂
25 0.       1.80e+ 1   9.00e 1 ▇▁▁▁▁
26 0.       3.50e+ 1   3.65e 2 ▇▁▁▁▁
27 5.00e+ 0 1.50e+ 1   6.37e 2 ▇▁▁▁▁
28 0.       3.00e+ 0   3.76e 2 ▇▁▁▁▁
29 9.70e+ 1 1.00e+ 2   1.00e 2 ▁▁▁▁▇
30 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
31 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
32 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
33 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
34 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
35 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
36 1.00e+ 0 1.00e+ 0   2.81e 2 ▇▁▁▁▁
37 1.00e+ 0 1.00e+ 0   2.81e 2 ▇▁▁▁▁
38 0.       0.         1.20e 1 ▇▁▁▁▁
39 0.       0.         1.30e 1 ▇▁▁▁▁
40 2.80e- 1 5.90e- 1   3.06e 1 ▇▁▁▁▁
41 1.12e+ 0 6.59e+ 0 Inf       ▇▁▁▁▁
42 1.51e+ 2 1.75e+ 2   2.10e 2 ▂▃▃▅▇

listings %<>%
    mutate(across(is_character, ~ifelse(.x == "", NA, .x)))

Misssing data

library(VIM)

listings %>% 
  select(host_is_superhost, review_scores_rating, host_response_time, name, host_since,zipcode) %>%
  aggr(numbers = TRUE, prop = c(TRUE, FALSE))

Best party place

listings %<>% 
  mutate(party_place = accommodates >= 10) 

listings %>% 
  filter(party_place == TRUE) %>%
  group_by(neighbourhood_cleansed) %>%
  summarize(n = n(),
         review = review_scores_rating %>% mean(na.rm = TRUE),
         price = price %>% mean(na.rm = TRUE) ) %>%
  arrange(desc(n))

EDA

DataViz

Geoplotting

library(leaflet)

listings %>% leaflet() %>%
  addTiles() %>%
  addMarkers(~longitude, ~latitude,
             labelOptions = labelOptions(noHide = F),
             clusterOptions = markerClusterOptions(),
             popup = paste0("<b> Name: </b>", listings$name, 
                            "<br/><b> Host Name: </b>", listings$host_name, 
                            "<br> <b> Price: </b>", listings$price, 
                            "<br/><b> Room Type: </b>", listings$room_type, 
                            "<br/><b> Property Type: </b>", listings$property_type
                 )) %>% 
#  setView(-74.00, 40.71, zoom = 12) %>%
  addProviderTiles("CartoDB.Positron")

# I need to fortify the data AND keep trace of the commune code! (Takes ~2 minutes)
library(broom)
neighbourhoods_tidy <-  neighbourhoods_geojson %>%
  tidy(region = "neighbourhood")

neighbourhoods_tidy %>% glimpse()

Rows: 6,658
Columns: 7
$ long  <dbl> 12.63094, 12.63126, 12.63221, 12.63160, 12.63154, 12.63153, 12.63153, 12.63153, 12.63157, 12.63158, 12…
$ lat   <dbl> 55.67050, 55.67028, 55.66961, 55.66943, 55.66941, 55.66940, 55.66939, 55.66930, 55.66926, 55.66924, 55…
$ order <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,…
$ hole  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
$ piece <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ group <fct> Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1…
$ id    <chr> "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st"…

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  theme_void() +
  coord_map()

neighborhood_agg <- listings %>%
  group_by(neighbourhood_cleansed) %>%
  summarise(n = n(),
            price_mean = price %>% mean(na.rm = TRUE),
            review_mean = review_scores_rating %>% mean(na.rm = TRUE))
  

neighbourhoods_tidy %<>%
  left_join(neighborhood_agg, by = c('id' = 'neighbourhood_cleansed'))

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = n)) +
  geom_polygon() +
  theme_void() +
  coord_map()